1.1. Mathematics and computer sciences [mathematics and other allied fields: computer sciences and other allied subjects (software development only hardware development should be classified in the engineering fields)]

50,0

4. Natural Sciences and Engineering

4.6. Computer Sciences

P170 Computer science, numerical analysis, systems, control

1.1. Mathematics and computer sciences [mathematics and other allied fields: computer sciences and other allied subjects (software development only hardware development should be classified in the engineering fields)]

Fueled by developments in deep learning computer vision has recently achieved spectacular improvements. At the same time human vision still holds important lessons for computer systems to achieve generalization and robustness. Despite their successes it is unknown how human and artificial neural networks for vision relate to each other. The target of this project is to narrow this gap by transferring knowledge and computational strategies between biological and artificial systems of vision. To that end, we will capitalize on unique properties of intracranial brain recordings to compare biological and deep learning systems for vision. In particular, we will determine the neural correlates of hierarchical visual processing, compare neural plasticity rules with optimization algorithms, and explore how prior knowledge can enhance natural and artificial vision. Resolving these issues is important to understand how vision occurs in the brain and to produce human-like vision algorithms.